Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable\nrobots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects\nthe human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion\nintention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition\nthat is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: \nsEMGdrivenmusculoskeletal (MS)model based motion intention recognition and machine learning (ML)model basedmotion intention\nrecognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a\ndiscussion of the existing problems in the current studies, major advances, and future challenges is presented.
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